An inexact proximal point algorithm using quasi-distances is introduced to give a solution of a minimization problem in the Euclidean space. This algorithm has been motivated by the proximal method introduced by Attouch, Bolte and Svaiter [1] but in this case we consider quasi-distance instead of the Euclidean distance, functions satisfying the Kurdyka-Lojasewicz inequality, vector errors in the critical point of the proximal subproblems. We obtain, under some additional assumptions, the global convergence of the sequence generated by the algorithm to a critical point of the problem.

The paper introduces a proximal point algorithm for solving equilibrium problems on convex sets with quasimonotone bifunctions in Hilbert spaces using Bregman distances. Supposing appropriate hypothesis on the model, this paper proves that the sequence of points which are generated for the algorithm converges weakly to certain solution point of the equlibrium problem.